A Prediction Framework for Distributed Data Stream Processing

It is very important in a lot of applications to forecast future trend of data streams. For example, a GPS system in a car could send not only the current location of the car but also its vector of movement or expected trajectory. Recent works on query processing over data streams mainly focused on approximate queries over newly arriving data. To the best of the knowledge, there is nothing to date in the literature on predictive query processing over data streams. Prediction models are introduced in distributed data stream processing and the problem formulation is detailed with. A common framework is raised and key parts of the architecture are described. The framework provides a mechanism to maintain adaptive prediction models that significantly reduce communication cost over the distributed environment while still guaranteeing sufficient precision of query results.